What do you need to succeed in working with Big Data? RedMonk analyst Donnie Berkholz will present quantitative research on the state of the field, covering the breadth of languages, tools, and infrastructure, to show you which choices to make today and which ones you'll need to get ready for, soon.

1. Tools and tech
for Big Data success
Donnie Berkholz, Ph.D.
IT Industry Analyst
@dberkholz
[Imagine cheesy clipart of hammers, silicon, etc.]

2. Tools and tech — huh?
● Languages
● Infrastructure
● Tooling
● It's not about the what, it's about the how

3. Thesis: Technology adoption
is increasingly bottom-up

4. The new kingmakers

5. The best DX wins

6. It's all about barriers to entry

7. Ecosystems matter

8. The challenges
IT pros delivering data solutions
60/36 IT/biz
Talend, summer 2012

9. The skills gap
NVP survey of execs, summer 2012

10. What can we learn from book sales?

12. What about the data analysts?
KDNuggets, summer 2012

13. What about the data analysts?
KDNuggets, summer 2012

14. It's not all about popularity...

15. The real growth isn't in SQL interfaces

16. Developers vs. marketers
Google Trends

17. IT pros delivering data solutions
60/36 IT/biz
Talend, summer 2012

18. Hadoop distributions: popularity
Google Trends: $VENDOR hadoop

20. Developers choose what's easiest

22. How to run: bare metal / DC, private
cloud, public cloud

23. Storing Data: HDFS, Ceph, Gluster

24. Open Building Blocks but not Open
Source: EMR on AWS

25. Realtime: Impala, Druid

26. In-memory: Redis, Memcached

27. Streaming: Storm, S4

28. Other Options: Hadoop YARN,
HPCC, Cassandra, Mongo, Riak

29. Other Options: Spark/Shark/Mesos

30. Abstraction: Mortar, Continuuity,
Qubole, Concurrent Lingual, etc.

31. Conclusion: KISS

32. Donnie Berkholz, Ph.D.
Analyst, RedMonk
@dberkholz
Disclosure: 10gen, Amazon, Basho, Cloudera,
Continuuity, IBM, MapR, Microsoft, and VMware are
clients.